Unlike soft models, the hard ones do not need any assumption for the tasks' relation; they do this internally.
Thus, some MTL models [1]}, [2]} use a cascade-based approach to learn a task from the previous one.
However, this approach restricts the feature space.
Accordingly, models [3]}, [4]} (with independent tasks) focus on merging multiple loss functions (depending on the task) to ensure the convergence of the models and robustness to noise [5]}.
Some models [6]}, [7]}, [8]} combined semantic segmentation with geometric information and others [9]}, [10]} with depth.
Other models [11]}, [9]}, [13]} measured and adjusted the degree of uncertainty of the samples along with the segmentation.
We noted that the uncertainty (i.e., either due to noise at the capture or due to the prediction's degree of confidence) is related to the segmented objects' edges.
Similarly, previous works [14]}, [15]} also used this line of reasoning by merging semantic contours with edge detection using the multi-scale feature in Acuna et al.'s [16]} work or adjusting the contours across the entire network as done by Cheng et al.  [14]} (i.e., multiple losses for detection).
Finally, the combination of SS with edge detection (in models [18]}) showed significant improvements for the segmentation task.
These models (CCL [19]}, CGBNet [20]}) generally present an hourglass architecture with skip connections for the edge detection map.
